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Found 5 papers, 4 papers with code
Intrinsic fluctuations of reinforcement learning promote cooperation
We find that next to a high caring for future rewards, a low exploration rate, and a small learning rate, it is primarily intrinsic stochastic fluctuations of the reinforcement learning process which double the final rate of cooperation to up to 80%.
Modeling the effects of environmental and perceptual uncertainty using deterministic reinforcement learning dynamics with partial observability
We find that partial observability creates unintuitive benefits in a number of specific contexts, pointing the way to further research on a general understanding of such effects.
Deep reinforcement learning in World-Earth system models to discover sustainable management strategies
Increasingly complex, non-linear World-Earth system models are used for describing the dynamics of the biophysical Earth system and the socio-economic and socio-cultural World of human societies and their interactions.
Deterministic limit of temporal difference reinforcement learning for stochastic games
Reinforcement learning in multi-agent systems has been studied in the fields of economic game theory, artificial intelligence and statistical physics by developing an analytical understanding of the learning dynamics (often in relation to the replicator dynamics of evolutionary game theory).
Multiagent Systems
Parsimonious modeling with Information Filtering Networks
We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors.
